Transparent roaming in virtual access point (VAP) enabled networks

ABSTRACT

In one embodiment, a supervisory device in a network forms a virtual access point (VAP) for a node in the network. A set of access points (APs) in the network are mapped to the VAP as part of a VAP mapping and the node treats the APs in the VAP mapping as a single AP for purposes of communicating with the network. The supervisory device receives measurements from the APs in the VAP mapping regarding communications associated with the node. The supervisory device identifies a movement of the node based on the received measurements from the APs in the VAP mapping. The supervisory device adjusts the set of APs in the VAP mapping based on the identified movement of the node.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/499,201, filed on Apr. 27, 2017, which claims priority to U.S.Provisional Patent Appl. No. 62/415,387, filed on Oct. 31, 2016, bothentitled TRANSPARENT ROAMING IN VIRTUAL ACCESS POINT (VAP) ENABLEDNETWORKS, by Thubert, et al., the contents of each of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, moreparticularly, to transparent roaming in virtual access point (VAP)enabled networks.

BACKGROUND

The Internet of Things (IoT) is the internetworking of devices orobjects (a.k.a., “things”, e.g., sensors, actuators, nodes, vehicles,etc.) that collect and exchange data, control objects, and process data.Many IoT networks are formed on low-power lossy networks (LLNs), andutilize carrier sense multiple access with collision avoidance (CSMA/CA)techniques. CSMA/CA, notably, is a communication technique that usescarrier sensing, where nodes attempt to avoid collisions by transmittingonly when the channel is sensed to be “idle.”

In general, deterministic routing concerns ensuring that messages (e.g.,packets) definitively arrive at a destination at a specific time orwithin a specified time range. However, implementing determinism inhub-and-spoke IoT models, particularly with CSMA/CA, faces a litany ofdrawbacks, such as overwhelming a server with multiple copies oftraffic, excessive delay, surges in latency, and unacceptable frameloss.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein may be better understood by referring to thefollowing description in conjunction with the accompanying drawings inwhich like reference numerals indicate identically or functionallysimilar elements, of which:

FIG. 1 illustrates an example communication network;

FIG. 2 illustrates an example network device/node;

FIGS. 3A-3D illustrate an example of virtual access point (VAP)formation;

FIGS. 4A-4B illustrate an example of the operation of a VAP;

FIGS. 5A-5C illustrate an example of adjusting a VAP mapping;

FIGS. 6A-6E illustrate an example of transparent roaming in aVAP-enabled network;

FIGS. 7A-7B illustrate an example of using feedback to adjustpredictions; and

FIG. 8 illustrates an example simplified procedure for performingtransparent roaming in a VAP.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one or more embodiments of the disclosure, a supervisorydevice in a network forms a virtual access point (VAP) for a node in thenetwork. A set of access points (APs) in the network are mapped to theVAP as part of a VAP mapping and the node treats the APs in the VAPmapping as a single AP for purposes of communicating with the network.The supervisory device receives measurements from the APs in the VAPmapping regarding communications associated with the node. Thesupervisory device identifies a movement of the node based on thereceived measurements from the APs in the VAP mapping. The supervisorydevice adjusts the set of APs in the VAP mapping based on the identifiedmovement of the node.

Description

A computer network is a geographically distributed collection of nodesinterconnected by communication links and segments for transporting databetween end nodes, such as personal computers and workstations, or otherdevices, such as sensors, etc. Many types of networks are available,ranging from local area networks (LANs) to wide area networks (WANs).LANs typically connect the nodes over dedicated private communicationslinks located in the same general physical location, such as a buildingor campus. WANs, on the other hand, typically connect geographicallydispersed nodes over long-distance communications links, such as commoncarrier telephone lines, optical lightpaths, synchronous opticalnetworks (SONET), synchronous digital hierarchy (SDH) links, orPowerline Communications (PLC), and others. Other types of networks,such as field area networks (FANs), neighborhood area networks (NANs),personal area networks (PANs), etc. may also make up the components ofany given computer network.

In various embodiments, computer networks may include an Internet ofThings network. Loosely, the term “Internet of Things” or “IoT” (or“Internet of Everything” or “IoE”) refers to uniquely identifiableobjects (things) and their virtual representations in a network-basedarchitecture. In particular, the IoT involves the ability to connectmore than just computers and communications devices, but rather theability to connect “objects” in general, such as lights, appliances,vehicles, heating, ventilating, and air-conditioning (HVAC), windows andwindow shades and blinds, doors, locks, etc. The “Internet of Things”thus generally refers to the interconnection of objects (e.g., smartobjects), such as sensors and actuators, over a computer network (e.g.,via IP), which may be the public Internet or a private network.

Often, IoT networks operate within a shared-media mesh networks, such aswireless or PLC networks, etc., and are often on what is referred to asLow-Power and Lossy Networks (LLNs), which are a class of network inwhich both the routers and their interconnect are constrained. That is,LLN devices/routers typically operate with constraints, e.g., processingpower, memory, and/or energy (battery), and their interconnects arecharacterized by, illustratively, high loss rates, low data rates,and/or instability. IoT networks are comprised of anything from a fewdozen to thousands or even millions of devices, and supportpoint-to-point traffic (between devices inside the network),point-to-multipoint traffic (from a central control point such as a rootnode to a subset of devices inside the network), and multipoint-to-pointtraffic (from devices inside the network towards a central controlpoint).

Fog computing is a distributed approach of cloud implementation thatacts as an intermediate layer from local networks (e.g., IoT networks)to the cloud (e.g., centralized and/or shared resources, as will beunderstood by those skilled in the art). That is, generally, fogcomputing entails using devices at the network edge to provideapplication services, including computation, networking, and storage, tothe local nodes in the network, in contrast to cloud-based approachesthat rely on remote data centers/cloud environments for the services. Tothis end, a fog node is a functional node that is deployed close to fogendpoints to provide computing, storage, and networking resources andservices. Multiple fog nodes organized or configured together form a fogsystem, to implement a particular solution. Fog nodes and fog systemscan have the same or complementary capabilities, in variousimplementations. That is, each individual fog node does not have toimplement the entire spectrum of capabilities. Instead, the fogcapabilities may be distributed across multiple fog nodes and systems,which may collaborate to help each other to provide the desiredservices. In other words, a fog system can include any number ofvirtualized services and/or data stores that are spread across thedistributed fog nodes. This may include a master-slave configuration,publish-subscribe configuration, or peer-to-peer configuration.

Low power and Lossy Networks (LLNs), e.g., certain sensor networks, maybe used in a myriad of applications such as for “Smart Grid” and “SmartCities.” A number of challenges in LLNs have been presented, such as:

1) Links are generally lossy, such that a Packet Delivery Rate/Ratio(PDR) can dramatically vary due to various sources of interferences,e.g., considerably affecting the bit error rate (BER);

2) Links are generally low bandwidth, such that control plane trafficmust generally be bounded and negligible compared to the low rate datatraffic;

3) There are a number of use cases that require specifying a set of linkand node metrics, some of them being dynamic, thus requiring specificsmoothing functions to avoid routing instability, considerably drainingbandwidth and energy;

4) Constraint-routing may be required by some applications, e.g., toestablish routing paths that will avoid non-encrypted links, nodesrunning low on energy, etc.;

5) Scale of the networks may become very large, e.g., on the order ofseveral thousands to millions of nodes; and

6) Nodes may be constrained with a low memory, a reduced processingcapability, a low power supply (e.g., battery).

In other words, LLNs are a class of network in which both the routersand their interconnect are constrained: LLN routers typically operatewith constraints, e.g., processing power, memory, and/or energy(battery), and their interconnects are characterized by, illustratively,high loss rates, low data rates, and/or instability. LLNs are comprisedof anything from a few dozen and up to thousands or even millions of LLNrouters, and support point-to-point traffic (between devices inside theLLN), point-to-multipoint traffic (from a central control point to asubset of devices inside the LLN) and multipoint-to-point traffic (fromdevices inside the LLN towards a central control point).

An example implementation of LLNs is an “Internet of Things” network.Loosely, the term “Internet of Things” or “IoT” may be used by those inthe art to refer to uniquely identifiable objects (things) and theirvirtual representations in a network-based architecture. In particular,the next frontier in the evolution of the Internet is the ability toconnect more than just computers and communications devices, but ratherthe ability to connect “objects” in general, such as lights, appliances,vehicles, HVAC (heating, ventilating, and air-conditioning), windows andwindow shades and blinds, doors, locks, etc. The “Internet of Things”thus generally refers to the interconnection of objects (e.g., smartobjects), such as sensors and actuators, over a computer network (e.g.,IP), which may be the Public Internet or a private network. Such deviceshave been used in the industry for decades, usually in the form ofnon-IP or proprietary protocols that are connected to IP networks by wayof protocol translation gateways. With the emergence of a myriad ofapplications, such as the smart grid advanced metering infrastructure(AMI), smart cities, and building and industrial automation, and cars(e.g., that can interconnect millions of objects for sensing things likepower quality, tire pressure, and temperature and that can actuateengines and lights), it has been of the utmost importance to extend theIP protocol suite for these networks.

FIG. 1 is a schematic block diagram of an example simplified computernetwork 100 illustratively comprising nodes/devices at various levels ofthe network, interconnected by various methods of communication. Forinstance, the links may be wired links or shared media (e.g., wirelesslinks, PLC links, etc.) where certain nodes, such as, e.g., routers,sensors, computers, etc., may be in communication with other devices,e.g., based on connectivity, distance, signal strength, currentoperational status, location, etc.

Specifically, as shown in the example network 100, three illustrativelayers are shown, namely the cloud 110, fog 120, and IoT device 130.Illustratively, the cloud 110 may comprise general connectivity via theInternet 112, and may contain one or more datacenters 114 with one ormore centralized servers 116 or other devices, as will be appreciated bythose skilled in the art. Within the fog layer 120, various fognodes/devices 122 (e.g., with fog modules, described below) may executevarious fog computing resources on network edge devices, as opposed todatacenter/cloud-based servers or on the endpoint nodes 132 themselvesof the IoT layer 130. Data packets (e.g., traffic and/or messages sentbetween the devices/nodes) may be exchanged among the nodes/devices ofthe computer network 100 using predefined network communicationprotocols such as certain known wired protocols, wireless protocols, PLCprotocols, or other shared-media protocols where appropriate. In thiscontext, a protocol consists of a set of rules defining how the nodesinteract with each other.

Those skilled in the art will understand that any number of nodes,devices, links, etc. may be used in the computer network, and that theview shown herein is for simplicity. Also, those skilled in the art willfurther understand that while the network is shown in a certainorientation, the network 100 is merely an example illustration that isnot meant to limit the disclosure.

Data packets (e.g., traffic and/or messages) may be exchanged among thenodes/devices of the computer network 100 using predefined networkcommunication protocols such as certain known wired protocols, wirelessprotocols (e.g., IEEE Std. 802.15.4, Wi-Fi, Bluetooth®, DECT-Ultra LowEnergy, LoRa, etc.), PLC protocols, or other shared-media protocolswhere appropriate. In this context, a protocol consists of a set ofrules defining how the nodes interact with each other.

FIG. 2 is a schematic block diagram of an example node/device 200 thatmay be used with one or more embodiments described herein, e.g., as anyof the nodes or devices shown in FIG. 1 above or described in furtherdetail below. The device 200 may comprise one or more network interfaces210 (e.g., wired, wireless, PLC, etc.), at least one processor 220, anda memory 240 interconnected by a system bus 250, as well as a powersupply 260 (e.g., battery, plug-in, etc.).

The network interface(s) 210 include the mechanical, electrical, andsignaling circuitry for communicating data over links 105 coupled to thenetwork 100. The network interfaces may be configured to transmit and/orreceive data using a variety of different communication protocols. Note,further, that the nodes may have two different types of networkconnections 210, e.g., wireless and wired/physical connections, and thatthe view herein is merely for illustration. Also, while the networkinterface 210 is shown separately from power supply 260, for PLC thenetwork interface 210 may communicate through the power supply 260, ormay be an integral component of the power supply. In some specificconfigurations the PLC signal may be coupled to the power line feedinginto the power supply.

The memory 240 comprises a plurality of storage locations that areaddressable by the processor 220 and the network interfaces 210 forstoring software programs and data structures associated with theembodiments described herein. Note that certain devices may have limitedmemory or no memory (e.g., no memory for storage other than forprograms/processes operating on the device and associated caches). Theprocessor 220 may comprise hardware elements or hardware logic adaptedto execute the software programs and manipulate the data structures 245.Operating system 242, portions of which is typically resident in memory240 and executed by the processor, functionally organizes the device by,inter alia, invoking operations in support of software processes and/orservices executing on the device. These software processes and/orservices may comprise routing process/services 244 and an illustrativevirtual access point (VAP) process 248, as described herein. Note thatwhile VAP process 248 is shown in centralized memory 240, alternativeembodiments provide for the process to be specifically operated withinthe network interfaces 210, such as a component of a MAC layer (e.g.,process 248 a).

It will be apparent to those skilled in the art that other processor andmemory types, including various computer-readable media, may be used tostore and execute program instructions pertaining to the techniquesdescribed herein. Also, while the description illustrates variousprocesses, it is expressly contemplated that various processes may beembodied as modules configured to operate in accordance with thetechniques herein (e.g., according to the functionality of a similarprocess). Further, while the processes have been shown separately, thoseskilled in the art will appreciate that processes may be routines ormodules within other processes.

In general, VAP process 248 includes computer executable instructionsthat, when executed by processor(s) 220, cause device 200 to performoperations regarding the formation, adjustment, and operation of a VAPwithin the network. These operations are described in greater detailbelow. In some embodiments, process 248 may employ any number of machinelearning techniques, to perform these operations. In general, machinelearning is concerned with the design and the development of techniquesthat receive empirical data as input (e.g., data regarding theperformance/characteristics of the network) and recognize complexpatterns in the input data. For example, some machine learningtechniques use an underlying model M, whose parameters are optimized forminimizing the cost function associated to M, given the input data. Forinstance, in the context of classification, the model M may be astraight line that separates the data into two classes (e.g., labels)such that M=a*x+b*y+c and the cost function is a function of the numberof misclassified points. The learning process then operates by adjustingthe parameters a,b,c such that the number of misclassified points isminimal. After this optimization/learning phase, process 248 can use themodel M to classify new data points, such as information regarding theperformance/characteristics associated with an established VAP or itsunderlying APs, to adjust the VAP, accordingly. Often, M is astatistical model, and the cost function is inversely proportional tothe likelihood of M, given the input data.

In various embodiments, VAP process 248 may employ one or moresupervised, unsupervised, or semi-supervised machine learning models toanalyze traffic flow data. Generally, supervised learning entails theuse of a training dataset, which is used to train the model to applylabels to the input data. For example, the training data may includesample network data that may be labeled simply as representative of a“good connection” or a “bad connection.” On the other end of thespectrum are unsupervised techniques that do not require a training setof labels. Notably, while a supervised learning model may look forpreviously seen network data that has been labeled accordingly, anunsupervised model may instead look to whether there are sudden changesin the performance of the network and/or the VAP. Semi-supervisedlearning models take a middle ground approach that uses a greatlyreduced set of labeled training data.

Example machine learning techniques that process 248 can employ mayinclude, but are not limited to, nearest neighbor (NN) techniques (e.g.,k-NN models, replicator NN models, etc.), statistical techniques (e.g.,Bayesian networks, etc.), clustering techniques (e.g., k-means,mean-shift, etc.), neural networks (e.g., reservoir networks, artificialneural networks, etc.), support vector machines (SVMs), logistic orother regression, Markov models or chains, principal component analysis(PCA) (e.g., for linear models), multi-layer perceptron (MLP) ANNs(e.g., for non-linear models), replicating reservoir networks (e.g., fornon-linear models, typically for time series), random forestclassification, or the like.

The performance of a machine learning model can be evaluated in a numberof ways based on the number of true positives, false positives, truenegatives, and/or false negatives of the model. For example, the falsepositives of the model may refer to the number of times the modelincorrectly labeled a connection as bad. Conversely, the false negativesof the model may refer to the number of connections that the modellabels as ‘good,’ but are, in fact, of poor quality to the user orendpoint node. True negatives and positives may refer to the number oftimes the model correctly classifies a connection as good or bad,respectively. Related to these measurements are the concepts of recalland precision. Generally, recall refers to the ratio of true positivesto the sum of true positives and false negatives, which quantifies thesensitivity of the model. Similarly, precision refers to the ratio oftrue positives the sum of true and false positives. In some cases,process 248 may also use reinforcement learning techniques whichgenerally act to use feedback about the ML predictions, to adjust theunderlying model. For example, an indication of a false positive from anexpert (e.g., a supervisory system or user) may be used to adjust theunderlying model, to avoid such predictive mistakes in the future.

As mentioned above, various protocols have been established for the IoT,including, in particular, various “hub-and-spoke” models, such asBluetooth Low Energy, DECT-Ultra Low Energy, IEEE 802.15.4 (with nomeshing), and Low Power Wi-Fi. However, there is also a lack ofdeterminism in these models due to prevalent use of carrier sensemultiple access with collision avoidance (CSMA/CA) for wirelesscommunications. Notably, deterministic networking requires that theworst-case data loss and latency should be guaranteed in a consistentfashion as multiple services are deployed on a common converged networkinfrastructure. This determinism is key to many applications, such assafety and process control. To complicate matters further, consideringthe vast amounts of devices that are currently being installed invarious IoT networks, an important constraint to be placed on anysolution is that changes at the end device (e.g., IoT device/thing)should not be necessary. This would also enable connection of legacydevices, thereby addressing a much wider market of applicability.

Virtual Access Point (VAP) Formation

The techniques herein introduce a methodology that can be used withexisting IoT infrastructure to implement a virtual access point (VAP)that is unique to a given IoT node. In general, the VAP is a logicalentity that appears to the endpoint node as a normal AP to which thenode associates as normal. In practice, however, the VAP is physicallydistributed over a number of APs surrounding the device. In anotheraspect, a supervisory device in the network may control the APmembership in the VAP, such as by transferring AP membership in thenetwork while the node moves and without requiring the node to roam. Infurther aspects, a machine learning model of the supervisory device mayoversee the VAP and adjust the VAP mapping, accordingly. Such a machinelearning model may reside in the supervisory device (e.g., controller,cloud service, etc.) in a centralized mode, or in a distributed manneracross the APs. Depending on the traffic criticality, more or less ofthose APs may copy a given frame received from the endpoint node to thesupervisory device.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with virtualaccess point process 248, which may include computer executableinstructions executed by processor 220 (or independent processor ofinterfaces 210) to perform functions relating to the techniquesdescribed herein. In particular, VAP process 248 may be a component ofan IoT device, a cloud device, or any other participating device for thetechniques described herein.

Specifically, according to one or more embodiments of the disclosure, asupervisory device in a network receives from a plurality of APs in thenetwork data regarding a network availability request broadcast by anode seeking to access the network and received by the APs in theplurality. The supervisory device uniquely associates the node with aVAP for the node and forms a VAP mapping between the VAP for the nodeand a set of the APs in the plurality selected based on the receiveddata regarding the network availability request. One of the APs in themapping is designated as a primary access point for the node. Thesupervisory device instructs the primary AP to send a networkavailability response to the node that includes information for the VAP.The node uses the information for the VAP to access the network via theset of APs in the VAP mapping.

Operationally, the techniques herein specify an architecture andprotocol between a supervisory device in a network (e.g., a controller,such as a network server) and a plurality of network access points(APs). The supervisory device may be configured for use with any of thetechnologies, noted above. Note that the techniques hereinillustratively place a number of functions in the supervisory device,such as components hosting Machine Learning (ML) processes, alsoreferred to as “learning machines”) that are mostlytechnology-independent. In some embodiments, these processes may beimplemented in a distributed manner (e.g., across different APs), inwhich case the collective set of APs may be viewed as the supervisorydevice.

More specifically, the techniques herein enable the formation and use ofa VAP that appears to always be “near” an endpoint node, so that qualityof transmission and PHY speed can be maintained. A VAP may beinstantiated within a set of APs around the node, and the set may varyautomatically under the control of the supervisory device (e.g., acontroller hosting a machine learning model) within the infrastructure.Thus, the device never needs to make a decision of roaming, which is aparadigm change in Wi-Fi and other wireless networks. Also, sincemultiple APs can receive a same packet at a same time with unrelatedprobabilities, the overall chances of reception are increased and delaysrelated to retries are reduced, thus helping with determinism.

Generally, a VAP may include any number of physical APs in the networkthat are placed in groups of equivalence, all using the same channel andsecurity settings. When a node (e.g., a STA) associates with thenetwork, the supervisory device (e.g., controller) may select a set ofAPs that together form a VAP for the associated node. The set may bechanged as the node moves. In this way, the endpoint node may experiencea constant service from the network and does not roam. For joining, thenode may interact with one of the APs (e.g., a primary networking AP)and may use a unique network identifier associated with the VAP, such asa unique service set identifier (SSID), a PAN-ID, or the like, that thenode uses to access the network. The supervisory device may alsooptimize the set of APs based on an objective function and on theobserved traffic in the network. Diversity is obtained because multiplemembers of the VAP may receive the same packet from the node, withrelatively independent chances of success.

The illustrative VAP protocol described herein also integrates well withdeterministic networking (DetNet). In particular, the VAP protocolenables multiple APs to receive a packet from an endpoint node at thesame time, creating a natural replication mechanism in which multiplecopies of a same packet may be captured, each by a different APparticipating in the VAP assigned to the node. Those APs may beconnected over different networks and the packet may circulate to therouter or the final destination over segregated paths. DetNetelimination may then be used to eliminate duplicate copies, if any. TheVAP protocol herein also leverages various networking techniques (e.g.,Wi-Fi, controllers, ML processes, etc.), to improve the user experiencewhile maintaining compatibility with the existing network standards andexisting endpoint nodes, by creating a virtual AP that is always thereand always optimized so the node never tries to roam.

FIGS. 3A-3D illustrate an example of virtual access point (VAP)formation, according to various embodiments. As shown in FIG. 3A, assumethat there exists a portion 300 of a network that includes a pluralityof APs 302 (e.g., APs 302 a-302 j) that are located in differentphysical locations through an area (e.g., a building, campus, etc.). APs302 may be in communication with a supervisory device 304 in thenetwork, such as a wireless controller, other networking device, or,alternatively, a remote device, such as a server located in a datacenter or cloud computing environment.

Now, assume that a node 306 is attempting to access the network. In sucha case, node 306 may broadcast a network availability request 308. Thespecific format of request 308 may be a function of the specificwireless protocols in use by the network. For example, in the case of802.11 networks, availability request 308 may be a probe request. Aswould be appreciated, from the standpoint of node 306, the expectedavailability response would include an identifier for the availablenetwork, such as the SSID of the Wi-Fi network. Reception ofavailability request 308 by any of APs 302 may trigger the formation ofa VAP for node 306, in various embodiments. In further embodiments, aVAP may also be formed at any time after node 306 associates with thenetwork.

In many network implementations, a plurality of APs 302 may receive thebroadcast network availability request 308 from node 306, with varyingresults. Notably, APs 302 within range of node 306 may, by virtue ofreceiving request 308, capture data regarding request 308, including thecontents of request 308 and other characteristics of the receivedrequest 308 such as, but not limited to, a link quality indicator (LQI),a signal to noise ratio (SNR), or received signal strength indicator(RSSI), or the like. For example, as shown, AP 302 f, which isphysically located closest to node 306, may determine that the qualityof the received request 308 is “excellent.” Similarly, APs 302 g, 302 c,and 302 e may determine that the quality of the received request 308 is“good,” “fair,” and “poor,” respectively.

In various embodiments, rather than simply responding to request 308with the requisite information needed for node 306 to associate with thenetwork, the receiving APs 302 may instead report the captured dataregarding request 308 to supervisory device 304. In particular, as shownin FIG. 3B, APs 302 c, 302 e, 302 f, and 302 g may send the data 310regarding the received network availability request 308 from node 306 tosupervisory device 304, thereby triggering the formation of a VAP fornode 306. Data 310 may include, for example, the identity of the sendingAP 302 that received request 308, a MAC address or other networkingdevice identifier for node 306, security status information, and/or atleast one metric of the quality of the communication with the node(e.g., signal strength, signal to noise ratio, signal quality, etc.).

In FIG. 3C, the supervisory device 304 may create a virtual AP (VAP)that is unique to node 306. In general, the VAP may be mapped to anynumber of APs 302 selected by supervisory device 304 based in part onthe data 310 regarding the availability request 308 received by thevarious APs 302. In other words, the VAP may be a logical construct thatis distributed over multiple APs 302. For example, even though APs 302c, 302 e, 302 f, and 302 g received association request 308 from node306, supervisory device 304 may determine that the set of APs 302 in theVAP mapping for node 306 should only include APs 302 c, 302 f, and 302g, based on the quality of the received request 308 (e.g., AP 302 e maybe excluded from the VAP based on the poor quality of the receivedbroadcast signal).

In addition to selecting the VAP member APs 302, supervisory device 304may also designate one of the selected APs 302 as the primary AP 302 forthe VAP. For example, based on data 310 regarding the availabilityrequest 308 sent by node 306, supervisory device 304 may determine thatAP 302 f has the best signal quality and should be the primary AP withinthe VAP for node 306. In turn, as shown in FIG. 3C, supervisory device304 may send instructions 312 to the selected APs 302 c, 302 f, and 302g that include information regarding the VAP. For example, instructions312 may instruct the selected APs 302 c, 302 f, and 302 g to belong tothe VAP for node 306, as identified initially by MAC address of node 306and, as soon as it is available, by security token, and/or othercryptographic methods.

To implement the VAP in the network, supervisory device 304 may employ aVAP protocol, in order to exchange configuration and data packets withthe APs 302. This protocol may be seen as an extension to existingprotocols, such as Lightweight Access Point Protocol (LWAPP) and Controland Provisioning of Wireless Access Points (CAPWAP), or may be specifiedas a new protocol. For example, the exchanges of data 310 andinstructions 312 may use this VAP protocol.

As shown in FIG. 3D, once the VAP has been generated and APs 302 c, 302f, and 302 g selected for mapping to the VAP, the primary AP 302 f maysend a network availability response 314 back to node 306. For example,response 314 may be a unicast probe response that has the sameinformation as a beacon frame. In various embodiments, response 314 sentby primary AP 302 f to node 306 may also include a unique networkidentifier associated with the VAP. For example, response 314 mayinclude an SSID or PAN-ID generated by supervisory device 304specifically for use with the VAP. In turn, node 306 may use thereceived response 314 to associate with the network, as it would undernormal conditions. From the standpoint of node 306, it is associatingwith the network normally via AP 302 f and may remain unaware of theexistence of its associated VAP.

FIGS. 4A-4B illustrate an example of the operation of a VAP afterformation, according to various embodiments. Continuing the examples ofFIGS. 3A-3D, when forming the VAP for node 306, supervisory device 304may also build an ordered list of the other APs 302 in the VAP mappingand conveyed to the selected APs 302 via instructions 312. In general,the ordered list may be used to add special diversity and improve thechances of a successful reception versus a retry from a given AP 302that has already failed once. For example, as shown in FIG. 4A, assumethat primary AP 302 f has sent a message to node 306, but that messagehas not been acknowledged by node 306. Under normal circumstances, AP302 f would then attempt to retry sending the message again to node 306.However, according to various embodiments, as shown in FIG. 4B, AP 302 gmay instead resend the message 402 to node 306. Because the resend issent from a different AP, the added spatial diversity increases thechances that the resent message 402 is received and acknowledged by node306.

In various embodiments, learning machines may play a key role in theassignment of APs to a VAP and/or in the adjustment of an existing VAP.For example, such a learning machine may be trained to select APs 302for inclusion in a given VAP based on a location estimation for thecorresponding node (e.g., from data 310) and/or the desired optimizationfor the traffic expected from that type of node. For example, thelocation of node 306 may be derived from a Time Difference of Arrivalvalue (TDOA or DTOA) and/or using triangulation between different APs302.

Such a traffic optimization may be based on a history of traffic for thetype of node, in some cases. As would be appreciated, the type ofapproach taken by the learning machine(s) may also vary with thespecific objective function for the traffic and node type. For example,assume that node 306 is a particular type of telepresence device andthat video traffic from similar devices has required a certain degree ofnetwork performance (e.g., in terms of drops, delay, jitter, etc.). Insuch a case, the learning machine may use its model for this node type,as well as any necessary metrics from the APs 302 in the network, toselect an appropriate VAP mapping to satisfy the objective function.Note that the objective function will also dramatically influence theset of metrics to be gathered from APs after the scanning phase but alsoduring the lifetime of a VAP. Indeed, according to the objectivefunction, the set of required features, in machine learning terms, mayvary, and the frequency of AP selection for the VAP will also vary.

FIGS. 5A-5C illustrate an example of adjusting a VAP mapping, accordingto various embodiments. Even after formation of a VAP for a given node,supervisory device 304 may continue to monitor and adjust the VAP, inorder to ensure that the objective function of the corresponding machinelearning model for node 306 continues to be met. To do so, the followingmessage types are introduced herein as part of the VAP protocol:

1.) VAP commands sent by the learning machine to APs 302, to gathermetrics of interest (e.g., network characteristics/statistics), reportthe VAP group membership, etc.

2.) VAP metrics sent by the responding APs 302 to the learning machineand used by the objective function to compute the VAP membership (e.g.,based on signal/noise ratio, signal strength, etc.).

3.) VAP stats reports sent by APs 302 to the learning machine and usedto evaluate the VAP efficiency. Such stats reports may be used to gatherstatistical data used by the learning machine to determine the“efficiency” of the VAP group, which can be used by the learning machineto consistently adjust the VAP according to an objective function.

4.) Learning machine stats sent by learning machine to a user interface,to report objective function efficiency results.

In various embodiments, for purposes of collecting networkmetrics/characteristics and VAP stats from APs 302, supervisory device304 may occasionally increase or otherwise adjust the APs in the VAPmapping. For example, as shown in FIG. 5A, supervisory device 304 mayinclude AP 302 e in the VAP mapping for purposes of data collectionafter formation of the VAP (e.g., via an instruction 312, even though AP302 e was not included in the original VAP mapping. This allows AP 302 eto collect and report information that would not otherwise be possibleusing the original APs of the VAP. For example, by changing the set ofAPs, the learning machine can change the TDOA listeners and optimize thedistance estimation, e.g., by adding APs while doing a measure, ortriangulating between different sets of APs. Such collected information502 may be reported to supervisory device 304, either on a push or pullbasis. After the data collection, supervisory device 304 may opt torevert the VAP mapping back to its previous membership or make furtheradjustments to the AP memberships, accordingly.

As shown in FIG. 5B, supervisory device 304 may also convey dataregarding the performance of the VAP (e.g., the objective function,etc.) to a user interface 504 for review by a user. In some embodiments,the learning machine may also use reinforcement learning to adjust itsmodel for node 306 and make changes to the VAP mapping, accordingly. Insuch a case, the user of interface 504 (e.g., an admin device, etc.) mayprovide feedback 506 to the learning machine about the VAP efficiency,which is then used by the LM to adjust the strategy adopted to computethe VAP mapping.

Based on the captured network characteristics from the APs, theperformance statistics for the VAP itself, and/or user feedback, themachine learning process may adjust the VAP by changing the APs in theVAP mapping. For example, as shown in FIG. 5C, assume that node 306 hasmove to a new physical location and is now in closer proximity to APs302 e and 302 g. In such a case, supervisory device 304 may select a newset of APs 302 for the VAP of node 306 and send out correspondinginstructions to the APs. Notably, as shown, supervisory device 304 mayselect a new set of APs for the VAP of node 306 that includes APs 302 e,302 f, and 302 g, with 302 g now designated as the primary AP.

Thus, the generated VAP may add spatial diversity to any CSMA/CA LLN, ina manner somewhat akin to that of the LoRa model. However, in contrastto the techniques herein, LoRa uses different MAC operations and doesnot support the faster speeds of the components (e.g., PHY) used intoday's IoT network devices. Additionally, LoRa does not support theassociation process, the use of identities (e.g., PAN-ID or SSID), andthe automatic repeat request (ARQ) process for acknowledgement andretry, which are supported using the techniques herein. Further, LoRadoes not support multicast communications, either.

Transparent Roaming in VAP-Enabled Networks

User experiences in wireless networks can vary considerably. Forinstance, in many networks today, when a node roams from one accesspoint (AP) to the next, as it moves, the node must de-associate from thefirst AP and re-associate with the next. During a transient periodbetween the break and the make, the service may be interrupted. Inparticular, during that transient time, the node may also scan for APsacross frequencies, introducing additional time of the blackout.

Even during a period where the mobile device is consistently attached tothe same AP, fluctuations in the radio quality and concurrent accessesto the medium can impact the observed quality by forcing the device todegrade the transmission rate or wait to get access to the medium. Forexample, when the number of IoT nodes increases on a givenchannel/frequency, the expected service may start to fail.

From the user perspective, the above situations lead to a noticeablevariation in the quality of the link, from non-functional to verydifferent speeds, all within short windows of time. In short, theexperienced quality lacks determinism, which bars a number ofoperational technology (OT) use cases, as well as impacting simple userexperiences such as online media.

On the other extreme, and as opposed to traditional markets that can beaddressed with Hi-power Wi-Fi, a number of IoT use cases requiredeterministic properties and, in particular, high reliability andbounded latency. Specific MAC operation (e.g., audio Bluetooth, TSCH,etc.) enable a deterministic MAC and are not subject to the techniquesherein. Rather, the techniques herein are interested in improving in theCSMA/CA operations that are typical in the considered standards.

In particular, network dynamics (e.g., the physics of radio frequencylinks, etc.) on the one hand, and determinism on the other hand, placecontradictory constraints on the network implementation. Notably, radiofluctuations are hardly predictable since they depend on the statisticalquality of the radio transmission and on erratic user movements. On theother hand, determinism ensures a consistent experience of the network,which requires resource reservations with precise timing over noncongruent paths to provide the best level of guarantees in terms ofdelivery ratio and latency. In deterministic IoT networks, such as6TiSCH networks, this tension is alleviated by the facts that: 1.) themedium is scheduled, so a guaranteed and timely access can be granted,and 2.) changes in a deterministic path (equivalent to a roamingdecision) are decided by a central path computation engine (PCE) asopposed to the device.

Unfortunately, CSMA/CA-based networks operate on the exact oppositepremises that:

-   -   1.) The roaming decision is made by the network node itself, as        opposed to the AP. This is done with no visibility on the actual        AP deployment vs. the node movement.    -   2.) The node adapts its transmission speed to adjust to the        transmission quality and limit the contention. A higher speed        reduces the footprint on the medium, but only if the        transmission succeeds. When it does not, consecutive retries are        a lot more harmful to the overall medium than sending the frame        at a lower speed in the first place. In case of low contention        on the medium, and if the gating factor for the overall speed is        the transport layer, a higher data-rate on the link layer is not        necessarily desirable anyway.    -   3.) The medium access is based on contention, and upon        collision, the device must decide when to try again. The optimum        depends on the number of contenders, but the method of        exponential backoff leaves it to chance.

Making all of the above decisions is very delicate for an IoT node orother node with limited processing power and no awareness of itsenvironment. The decision may depend on an overall objective, such aslimit latency by avoiding retries, or maximize data-rate or overallthroughput, but the node has no visibility on the intention of theapplication or the goals of the network administrator. For example, fromthe perspective of the node, it will not be able to distinguish betweena conference with a high density of users generating mail and Webtraffic and a home network with a few users streaming video or playinginteractive games.

The techniques herein, therefore, attempt to optimize the wirelessexperience by transferring the core decisions idea to Learning Machines(LMs), either located on premise, or in the cloud in a fashion that isbackward compatible with the standards such as 802.15.4 and Low PowerWi-Fi. Said differently, the techniques herein optimize the CSMA/CAexperience by transferring the core decisions idea to LMs in a fashionthat is backward compatible with the incumbent standard (e.g., Wi-Fi or802.15.4). In some aspects, LMs track node movement and dynamicallyassign the best APs to the VAP of the node. The VAP presents a constantposture from the perspective of the node, which never finds a reason toroam. As a result, the actual decisions such as which AP gets a packetfrom that node, or sends a packet to that node, are transferred to theinfrastructure and optimized by LMs. In another aspect, feedback loopsmay be employed to constantly adjust the AP selection in the VAPaccording to the approach used by the node to select a best AP and thusto avoid roaming, leading to improved determinism.

Note that IEEE 802.11r proposes roaming optimizations, but that is stilla node decision, unlike the techniques herein, where the node does notroam at all, but it is the APs mapped to the VAP that change, tomaintain a constant posture towards the node. Also, while some vendorsenable make-before-break handover for Wi-Fi, such techniques require anode equipped with two radio transceivers.

Specifically, according to various embodiments herein, a supervisorydevice in a network forms a virtual access point (VAP) for a node in thenetwork. A set of access points (APs) in the network are mapped to theVAP as part of a VAP mapping and the node treats the APs in the VAPmapping as a single AP for purposes of communicating with the network.The supervisory device receives measurements from the APs in the VAPmapping regarding communications associated with the node. Thesupervisory device identifies a movement of the node based on thereceived measurements from the APs in the VAP mapping. The supervisorydevice adjusts the set of APs in the VAP mapping based on the identifiedmovement of the node.

Illustratively, the techniques described herein may be performed byhardware, software, and/or firmware, such as in accordance with theillustrative “Virtual Access Point (VAP)” process 248, which may includecomputer executable instructions executed by the processor 220 (orindependent processor of interfaces 210) to perform functions relatingto the techniques described herein, e.g., in conjunction with routingprocess 244 or other processes as appropriate. In particular, the VAPprocess 248 may be a component of an IoT device, a cloud device, or anyother participating device for the techniques described herein.

Operationally, and with reference to the examples in FIGS. 6A-6E, thetechniques herein apply to an environment, such as a VAP-enabled CSMA/CAIoT network, or, more generally, to any other form of wireless networkwith multiple APs. Also, as done above, the description below uses LowPower Wi-Fi parlance of APs to refer to the fixed networkantennas/transceivers, though other similar terminology may be used forsimilar functionality.

According to the techniques herein, the APs that belong to the VAP of anode change dynamically to follow the node so that the node does notexperience a loss of service and does not attempt to roam. As a result,there is no window of time during re-association whereby the node istemporarily cut off from network services. Notably, roaming is avoidedby adjusting the AP(s) mapped to the VAP of the node using predictiveanalytics (machine learning) in such a way that there is no moreroaming.

In some embodiments, the APs in a node's VAP use the control VAPprotocol messages described previously, to tell thecontroller/supervisory device about measurements regardingcommunications associated with the node. Such measurements may comprisewireless signal information such as strength, quality, etc., or anyother information regarding the communications associated with the node.In turn, when the quality is degrading for one or more APs, thecontroller/supervisory device may select a number of new APs and placesthem temporarily in the VAP, as described previously with respect toFIG. 5A. If the measurements from these APs are better than that of theexisting VAP members, the supervisory device may adjust the VAP.

In various embodiments, as shown in FIG. 6A, supervisory device 304 mayreceive measurements regarding the communications 602 associated withnode 306 from the APs 302 in its vicinity. As noted, these measurementsmay include information regarding these communication signals such as alink quality indicator (LQI), a signal to noise ratio (SNR), a receivedsignal strength indicator (RSSI), or any other measurement regarding thecommunications 602. Using the techniques above, supervisory device 304may also vary the APs in the VAP mapping, as needed, to obtain testmeasurements from other APs 302 regarding communications 602, as well.Further measurements may also indicate, for example, retry information,such as when the primary AP in the VAP mapping (e.g., primary AP 302 f)fails to reach node 306 and a backup AP in the VAP mapping (e.g., AP 302g) retransmits the packet to node 306.

As shown in FIG. 6B, supervisory device 304 may use the receivedmeasurements regarding the communications associated with node 306 todetermine a movement of node 306. For example, consider the case in FIG.6A in which AP 302 c indicates that its signal from node 306 is at 15%quality and is decreasing, AP 302 f indicates that its signal from node306 is at 85% quality and is stable, and AP 302 g indicates that itssignal from node 306 is at 55% quality and is increasing. In such acase, as illustrated in FIG. 6B, this may indicate that node 306 ismoving away from the location of AP 302 c at a trajectory 606 and willbe located at future location 604 at a certain time.

In one embodiment, supervisory device 304 may use a time difference ofarrival (TDOA)-based approach, to identify the location of node 306 andits direction/trajectory of movement. In such an approach, themeasurements taken by the various APs 302 can be compared to one anotherfrom a timing standpoint, to help pinpoint the current location of node306. By tracking the current locations of node 306 over time,supervisory device 304 can determine the future location 604 andtrajectory 606 of node 306.

In some embodiments, supervisory device 304 may also leverage externalinformation such as matching node 306 with a particular user (e.g.,based on 802.1x) and then matching scheduling information for the userwith a given location. For example, data from a calendaring system orroom reservation system may indicate that the user will soon travel fromone meeting room to another and supervisory device 304 can use thisinformation to predict the trajectory that the user will take and when.

As shown in FIG. 6C, supervisory device 304 may predict an optimal VAPconfiguration based on the movement of node 306. For example,supervisory device 304 may derive new APs 302 for the VAP of node 306,based on the location and/or movement of node 306. Particularly, asshown, assume that supervisory device 304 predicts that node 306 will belocated at future location 604 at a certain time and moving alongtrajectory 606. In turn, supervisory device 304 may use one or more LMs,to predict the optimal set of APs in the VAP mapping for that locationand time. Notably, as node 306 moves away from APs 302 c and 302 f,supervisory device 304 may determine that these APs should be phased outof the VAP mapping. In addition, supervisory device 304 may determinethat the updated VAP mapping should include APs 302 g-302 j, with AP 302g acting as the new primary AP for the VAP.

LM techniques can be used to improve the chances of selecting the nextAPs as the node moves. For example, a user will probably follow a paththat is similar to one followed by previous users across open spacesfull of cubicles. The techniques herein provide the ability toconstantly predict the user movements in order to automaticallydetermine the best APs for the VAP mapping and avoid roaming by thenode.

In various embodiments, supervisory device 304 (e.g., a wirelesscontroller, a cloud-based service, etc.) may execute two machinelearning models in conjunction with one another: 1.) a trajectorytracking model responsible for predicting the location where node 306will be in the future, and 2.) a VAP configuration prediction model thatpredicts the optimal changes to the VAP mapping for that futurelocation. For example, such a configuration change may determine whichAP will be the preferred AP for the node at that future location. Notefurther that the configuration prediction may make use of differentstrategies to predict the best AP for a given node (e.g., on a per-nodebasis), since different nodes may use different AP selection strategies.In other words, the machine learning model may also take into accountthe AP selection approach taken by node 306, when predicting optimal VAPchanges for its future location.

For the sake of illustration, the movement prediction can be performedusing ML approaches such as Hidden Markov Chain, or Adaptive ResonanceTheory (ART). As far as predicting the VAP configuration, this can bespecified as a regression problem (e.g., with an objective functionbeing tied to a deterministic metrics such as time to transmit, andinput features will be all measurements available at the node itself).That being said, in the embodiments herein, the objective is not to findthe AP that will optimize determinism, but the AP that will likely beselected by node 306, so as to avoid roaming.

In FIG. 6D, supervisory device 304 may send VAP protocol instructions608 to the various APs in the network, to effect the VAP configurationchanges in accordance with its movement predictions for node 306. Forexample, AP 302 c may be removed from the VAP mapping at a certain time,AP 302 f may be removed from the VAP mapping at a subsequent time, etc.In addition, instructions 608 may add APs 302 g-302 j at the appropriatetimes, in advance of node 306 coming within range of the predictedlocation, as well as switching over the primary AP for the VAP from AP302 f to AP 302 g. In doing so, when node 306 reaches its predictedlocation, as shown in FIG. 6E, it will not resort to roaming, as the APsthat service its VAP also change.

FIGS. 7A-7B illustrate an example of using feedback to adjustpredictions. In networks such as Wi-Fi, it is the endpoint node thatmakes the decision to roam and the algorithm used to make such adecision is left to the node itself. As a matter of fact, strategies dovary between vendors of end devices. In some cases, roaming strategiesmay be simple and conservative (e.g., roam when the RSSI←70 dB). Inother cases, more sophisticated approaches are taken by consideringmultiple parameters and are sometimes fairly sensitive, in an attempt toconstantly select the best AP according to a set of given metrics.

Consequently, the LM making the predictions regarding the optimal VAPconfigurations for node 306 may need to be adapted on a per node basis.In one embodiment, the LM could be notified of the roaming strategy ofnode 306, so as to adapt the AP prediction model. In other embodiments,the LM may make use of different predicting AP models according to thenode type, or the node type may be a feature of the same model.

As shown in FIG. 7A, supervisory device 304 may receive feedback 702from the APs 302 regarding its predictive models. For example, if node306 is located at a different location than the predicted location, thisinformation can be used to update the location predictive model.Similarly, if node 306 roams to another AP, that AP may send a customVAP protocol message to supervisory device 304 to indicate this roaming.

Upon receiving the roaming notification feedback, as shown in FIG. 7B,the LM will deduce that the prediction strategy has failed (e.g., thepredicted AP is incorrect and thus the node has roamed despite theattempt of the LM to find the best strategy). Such feedback is a quitepowerful message for the LM so as to determine the overall efficiency ofthe system and trigger a relearning of the predictor, without any userintervention.

FIG. 8 illustrates an example simplified procedure for performingtransparent roaming in a VAP, according to various embodiments. Forexample, a non-generic, specifically configured device (e.g., device200) may perform procedure 800 by executing stored instructions (e.g.,process 248). Such a device may be, in some embodiments, a supervisorydevice such as a wireless controller in the network that oversees aplurality of APs in the network or, in further embodiments, a collectionof one or more APs or a cloud-based service. The procedure 800 may startat step 805 and continue on to step 810 where, as described in greaterdetail above, the device may form a virtual access point (VAP) in thenetwork for the node. A plurality of access points (APs) in the networkare mapped to the VAP and the APs in the VAP mapping are treated as asingle AP by the node for communicating with the network.

At step 815, as detailed above, the device may receive measurements fromthe APs in the VAP mapping regarding communications associated with thenode. For example, such measurements may include indications of thewireless signal quality, signal strength, or the like (e.g., RSSIvalues, LQI values, SNR values, etc.). In some embodiments, the devicemay vary the APs in the VAP mapping, as needed, to obtain testmeasurements from any number of other APs in the network. For example,as the node physically moves, it may come within reach of other APs.

At step 820, the device may identify a movement of the node based on thereceived measurements from step 815, as described in greater detailabove. In some embodiments, the device may use time different of arrival(TDOA) techniques, to compare the communications associated with thenode, as received by the various APs, to determine the location of thenode. In further embodiments, the device may receive schedulinginformation (e.g., from a room reservation system, a calendaring system,etc.), to determine a location of a user associated with the node.

At step 825, as detailed above, the device may adjust the APs in the VAPmapping of the node's VAP based on the identified movement of the node.In various embodiments, the device may use machine learning to predictfuture locations of the node and/or predict the optimal set of APs inthe VAP mapping for that future location. For example, the model maytake into account the roaming criteria used by the node (e.g., theinferred roaming strategy), so as to select a set of APs that willprevent the node from roaming as it nears its predicted future location.Procedure 800 then ends at step 830.

It should be noted that while certain steps within procedure 800 may beoptional as described above, the steps shown in FIG. 8 are merelyexamples for illustration, and certain other steps may be included orexcluded as desired. Further, while a particular order of the steps isshown, this ordering is merely illustrative, and any suitablearrangement of the steps may be utilized without departing from thescope of the embodiments herein.

The techniques described herein, therefore, provide transparent roamingin a VAP-enabled network. In particular, the techniques herein, allowsthe infrastructure to optimize the coverage for a moving node, resultingin more deterministic operations since the node never needs to roam andthere is no service interruption when the set of APs changes(transparently to the node).

While there have been shown and described illustrative embodiments thatprovide techniques related to virtual access points (VAPs), it is to beunderstood that various other adaptations and modifications may be madewithin the spirit and scope of the embodiments herein. For example,while certain embodiments are described herein with respect to usingcertain environments, such as the IoT, other embodiments need not belimited to IoT devices. In addition, while certain protocols are shown,such as Wi-Fi, 802.15.4, LoRa, etc., other suitable protocols may beused, accordingly. For instance, while the techniques herein generallyapply to a generalized CSMA/CA LLN, it should be specifically noted thatthe techniques can be applied to (based on) any of the standardsmentioned above or known in the art. For ease of understanding(expecting the reader to be more familiar with the Wi-Fi parlance), thedescription above uses the term AP in the more general sense of atransceiver in a network. However, with Bluetooth LE, the central roleillustratively maps to an AP, whereas the peripheral role is akin to theendpoint node. The same goes for the 802.15.4 PAN coordinator which issimilar to an AP, and the full-function device (FFD) or reduced-functiondevice (RFD) which illustratively map to an endpoint node, when 802.15.4is used in plain hub-and-spoke (in that case a PAN ID illustrativelyserves as SSID). With DECT-ULE, the DECT Fixed Part is illustrativelythe AP, and the Portable Part is illustratively the node.

Note that some protocols on Wi-Fi networking refer to a “virtual accesspoint” as many different things. For example, hosting several logicalAPs in one physical AP may be referred to as a “virtual access point”,while turning a PC into an AP may also be referred to as a “virtualaccess point”. The VAPs in this present disclosure should not beconfused with the shared terminology, and is completely different inthat one VAP herein is distributed over multiple physical APs, and therecan be one VAP per node.

The foregoing description has been directed to specific embodiments. Itwill be apparent, however, that other variations and modifications maybe made to the described embodiments, with the attainment of some or allof their advantages. For instance, it is expressly contemplated that thecomponents and/or elements described herein can be implemented assoftware being stored on a tangible (non-transitory) computer-readablemedium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructionsexecuting on a computer, hardware, firmware, or a combination thereof.Accordingly this description is to be taken only by way of example andnot to otherwise limit the scope of the embodiments herein. Therefore,it is the object of the appended claims to cover all such variations andmodifications as come within the true spirit and scope of theembodiments herein.

What is claimed is:
 1. A method, comprising: forming, by a supervisorydevice in a network, a virtual access point (VAP) for a node in thenetwork, wherein a set of access points (APs) in the network are mappedto the VAP during a VAP mapping, wherein the VAP mapping allows the APsto be treated by the node as a single AP to which the node connects, andwherein the node communicates with the network through the VAP;receiving, at the supervisory device and from the set of APs in the VAPmapping, measurements gathered by the APs regarding communicationsassociated with the node; identifying, by the supervisory device, amovement of the node and a predicted future location of the node basedon the received measurements from the set of APs in the VAP mapping; andadjusting, by the supervisory device, the set of APs in the VAP mappingusing a machine learning model that predicts an optimal set of changesto the set of APs in the VAP mapping based on the predicted futurelocation of the node.
 2. The method as in claim 1, wherein themeasurements from the set of APs in the VAP mapping comprise at leastone of: a signal strength or signal quality of the communicationsassociated with the node.
 3. The method as in claim 1, whereinidentifying the movement of the node based on the received measurementsfrom the set of APs in the VAP mapping comprises: computing, by thesupervisory device, a time difference of arrival of communications fromthe node received by the set of APs in the VAP mapping.
 4. The method asin claim 1, wherein identifying the movement of the node based on thereceived measurements from the set of APs in the VAP mapping comprises:matching, by the supervisory device, the node to a particular user; andreceiving, at the supervisory device, scheduling information regardingthe user, wherein the identified movement of the node is based in parton the scheduling information regarding the user.
 5. The method as inclaim 1, wherein the optimal set of changes includes an addition or aremoval of an AP from the set of APs in the VAP mapping.
 6. The methodas in claim 5, further comprising: receiving, at the supervisory device,a notification that the predicted optimal set of changes to the set ofAPs in the VAP mapping caused the node to roam; and retraining, by thesupervisory device the machine learning model used to predict theoptimal set of changes to the set of APs in the VAP mapping, based onthe received notification that the changes to the set of APs in the VAPmapping caused the node to roam.
 7. The method as in claim 5, whereinthe machine learning model to predict an optimal set of changes to theset of APs in the VAP mapping further bases the prediction on roamingcriteria used by the node to determine when to roam.
 8. The method as inclaim 1, further comprising: determining, by the supervisory device,that the measurements from the set of APs in the VAP mapping indicatereduced signal performance of the communications associated with thenode; and varying, by the supervisory device, the set of APs in the VAPmapping to obtain test measurements regarding the communicationsassociated with the node.
 9. An apparatus, comprising: one or morenetwork interfaces to communicate with a network; a processor coupled tothe one or more network interfaces and configured to execute a processcomprising program instructions stored in a memory; and the memoryconfigured to store the process comprising the program instructionsexecutable by the processor, the process when executed configured to:form a virtual access point (VAP) for a node in the network, wherein aset of access points (APs) in the network are mapped to the VAP during aVAP mapping that allows the APs to be treated by the node as a single APto which the node connects, and wherein the node communicates with thenetwork through the VAP; receive, from the set of APs in the VAPmapping, measurements gathered by the APs regarding communicationsassociated with the node; identify a movement of the node and apredicted future location of the node based on the received measurementsfrom the set of APs in the VAP mapping; and adjust the set of APs in theVAP mapping using a machine learning model that predicts an optimal setof changes to the set of APs in the VAP mapping based on the predictedfuture location of the node.
 10. The apparatus as in claim 9, whereinthe measurements from the set of APs in the VAP mapping comprise atleast one of: a signal strength or signal quality of the communicationsassociated with the node.
 11. The apparatus as in claim 9, wherein theapparatus identifies the movement of the node based on the receivedmeasurements from the set of APs in the VAP mapping by: computing a timedifference of arrival of communications from the node received by theset of APs in the VAP mapping.
 12. The apparatus as in claim 9, whereinthe apparatus identifies the movement of the node based on the receivedmeasurements from the set of APs in the VAP mapping by: matching thenode to a particular user; and receiving scheduling informationregarding the user, wherein the identified movement of the node is basedin part on the scheduling information regarding the user.
 13. Theapparatus as in claim 9, wherein the optimal set of changes includes anaddition or a removal of an AP from the set of APs in the VAP mapping.14. The apparatus as in claim 13, wherein the process when executed isfurther configured to: receive a notification that the predicted optimalset of changes to the set of APs in the VAP mapping caused the node toroam; and retrain the machine learning model used to predict the optimalset of changes to the set of APs in the VAP mapping, based on thereceived notification that the changes to the set of APs in the VAPmapping caused the node to roam.
 15. The apparatus as in claim 13,wherein the machine learning model to predict an optimal set of changesto the set of APs in the VAP mapping further bases the prediction onroaming criteria used by the node to determine when to roam.
 16. Theapparatus as in claim 9, wherein the process when executed is furtherconfigured to: determine that the measurements from the set of APs inthe VAP mapping indicate reduced signal performance of thecommunications associated with the node; and vary the set of APs in theVAP mapping to obtain test measurements regarding the communicationsassociated with the node.
 17. A tangible, non-transitory,computer-readable medium storing program instructions that, whenexecuted by a supervisory device in a network, cause the supervisorydevice to perform a process comprising: forming, by the supervisorydevice, a virtual access point (VAP) for a node in the network, whereina set of access points (APs) in the network are mapped to the VAP duringa VAP mapping, wherein the VAP mapping that allows the APs to be treatedby the node as a single AP to which the node connects, and wherein thenode communicates with the network through the VAP; receiving, at thesupervisory device and from the set of APs in the VAP mapping,measurements gathered by the APs regarding communications associatedwith the node; identifying, by the supervisory device, a movement of thenode and a predicted future location of the node based on the receivedmeasurements from the set of APs in the VAP mapping; and adjusting, bythe supervisory device, the set of APs in the VAP mapping using amachine learning model that predicts an optimal set of changes to theset of APs in the VAP mapping based on the predicted future location ofthe node.
 18. The tangible, non-transitory, computer-readable medium asin claim 17, wherein the optimal set of changes includes an addition ora removal of an AP from the set of APs in the VAP mapping.
 19. Thetangible, non-transitory, computer-readable medium as in claim 18,further comprising: receiving, at the supervisory device, a notificationthat the predicted optimal set of changes to the set of APs in the VAPmapping caused the node to roam; and retraining, by the supervisorydevice the machine learning model used to predict the optimal set ofchanges to the set of APs in the VAP mapping, based on the receivednotification that the changes to the set of APs in the VAP mappingcaused the node to roam.
 20. The tangible, non-transitory,computer-readable medium as in claim 18, wherein the machine learningmodel predicts an optimal set of changes to the set of APs in the VAPmapping further bases the prediction on roaming criteria used by thenode to determine when to roam.